PRAGMA — Revolut’s Finance Foundation Model
Summary: PRAGMA is Revolut’s proprietary foundation model trained on 24 billion banking events, delivering major uplifts across credit scoring, fraud, and marketing vs. prior ML models.
Sources: raw/articles/simon-taylor-2026-04-26.md, raw/call-notes/carlos-2026-05-10.md
Last updated: 2026-05-17
What It Is
PRAGMA is a behavioral foundation model — not an LLM. It doesn’t generate text. It reads sequences of customer events (logins, screen taps, payments) and learns rich representations of customer behavior.
- 26M customers, 24B customer events, 207B tokens
- Trained on 32 H100s in ~2 weeks
- Built with NVIDIA (NeMo AutoModel, cuDF for feature engineering)
- Replaces six separate custom ML models with one
Training Approach
Three experiments to validate the model:
- Pre-trained embeddings only — how much information is in the embedding before task-specific training?
- Embeddings + hand-crafted ML features — what does the foundation model capture that years of feature engineering missed?
- LoRA fine-tuning — can the foundation model beat the entire data science team by pressing a button? (Answer: mostly yes.)
Production Uplifts vs. Prior ML Models
| Use Case | Uplift |
|---|---|
| Credit scoring (PR-AUC) | +130% |
| Fraud recall | +65% |
| Marketing engagement | +79% |
| Product recommendation | +40% |
| Anti-money laundering | −47% (expected failure — AML is a network problem; PRAGMA reads users in isolation) |
Why AML Failed
AML is a network problem — what matters is who you transact with, not what you do. PRAGMA reads each user’s history in isolation and can’t see transaction chains. This is a known limitation; the team is working on the next architecture to address it.
What’s Next: Generative Finance Models
PRAGMA today is like BERT in 2020 — it reads and predicts. The next step is generative: a model that could write a customer’s future event sequence, simulating when they’ll take a product, then rewinding to identify what conditions led there and manufacturing those conditions.
Business Case
Napkin math on established institutions:
- JPMorgan has $10B+ annual credit costs. Even 10% of PRAGMA’s stated credit scoring gain → hundreds of millions/year.
- Every 5.75 in operational overhead. A 65% recall improvement pays for the GPU bill many times over.